Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

Peer ReviewedIntroduction Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and 1H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wide application. Objective Assessment of whether the statistical model established using a targeted urine metabolomics dataset for the differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) would be improved by normalization to osmolality instead of creatinine. Methods A metabolomics dataset from 51 patient urine samples previously analyzed using two liquid chromatography-tandem mass spectrometry methods was used. The data was normalized to creatinine and osmolality. The statistical analysis was achieved using partial least square discriminant analysis and models of separation were generated and compared. Results Creatinine and osmolality values in asthma and COPD patients were strongly correlated. Using the same metabolites, we found that normalization to osmolality did not significantly change the results. The metabolites of importance in separation remained the same for both methods. The statistical strength of the creatinine model was somewhat better than the osmolality normalized model (R2Q2=0.919, 0.705 vs R2Q2 =0.929, 0.671). Conclusion Our findings suggest that targeted urine metabolomics data can be normalized to creatinine or osmolality with no significant impact on the diagnostic accuracy of the model